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International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 01 | Jan-2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1436 Stock market Prediction & Analysis Ajinkya Hinge 1 , Himanshu Chavan 2 , Jatin Jantre 3 , Parikshit Jagtap 4 1,2,3,4 BE, Department of Computer Engineering, GESRHSCOE, Nashik, Maharashtra, India ----------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Many Proposed System have been done for stock market prediction. While there are many free and premium data sources available today, and there many new machine learning algorithms have been proposed, most of these researches do not focus on utilizing these data sources and algorithms. This paper proposes a stock market prediction service frame work that allows users to choose different data sources and machine learning techniques. In particular, while most existing prediction approaches are based on neural networks, support vector machines or Naive Bayes, we Proposed the flexibility of our framework by including metric learning based methods to predict stock movement. Key Words: SAR – Stop & Reverse, SMA- Simple Moving Average. INTRODUCTION In proposed system we present the reliable and stable online Stock Market Prediction based application The subject input and questions , difficulty level will given as input from user Respected data will be stored on backend which is cloud . On backend admin will be manage tests related database work While user uses desktop application and by using various strategies accurate prediction is provided BASIC IDEA The goal is to improve the decision making power and wakefulness about the investment in the stock market from the naïve user’s perspective. The naïve investors are having the problem of choosing the valuable stock. The reason for this concern is the lack of knowledge about the market and lack of education. It is very essentials to identify the stocks which are suitable for the investors’ Expectations and identify the suitable stocks for short term investors and long term investors based on the stock market analysis. Here We Proposed an easiest way to identify the stocks which is suitable for the investors’ expectations. It means that this research will give suitable stocks for short term investors and long term investors based on the data analysis and its wave pattern movement. And also it will give the details regarding risk associated with these stocks. IMPLEMENTATION These problem have many methodologies to solve like predicting stock with strategies like Simple moving average ,Parabolic SAR, Bolinger Bandes, Don chain Channels which gives prediction up to 70-80% while our application predicts up to 80-90% with somehow Accuracy. Examples: A Simple Moving Average is formed by computing the average price of security over a specific number of periods. Most moving averages are based on closing price .A 5-day simple moving average is the five sum of closing prices divided by five. As its name implies, a moving average is an average that moves Mathematical Model: SMA = (P[1] + P[2] + P[3] + . . . P[N]) ÷N Parabolic SAR - Parabolic Stop-and-Reverse, this indicator provides both entries and exits. The indicator is composed of a series of dots, either above or below the price. When dots move from below to above the price bars, it is time to get out of longs or get short. Extreme Point (EP) = The highest high of the current uptrend or the lowest low of the current downtrend. Acceleration Factor (AF) = Determines the sensitivity of the SAR. AF starts at .02 and increases by .02 every time the EP rises in a Rising SAR or EP falls in a Falling SAR. The calculations for Rising Parabolic SAR and Falling Parabolic SAR are different so they will be separated.

Transcript of Stock market Prediction & Analysis - irjet.net · PDF fileInternational Research Journal of...

Page 1: Stock market Prediction & Analysis - irjet.net · PDF fileInternational Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 01 | Jan-2018

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 05 Issue: 01 | Jan-2018 www.irjet.net p-ISSN: 2395-0072

© 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1436

Stock market Prediction & Analysis

Ajinkya Hinge1, Himanshu Chavan2, Jatin Jantre3, Parikshit Jagtap4

1,2,3,4 BE, Department of Computer Engineering, GESRHSCOE, Nashik, Maharashtra, India ----------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Many Proposed System have been done for stock market prediction. While there are many free and premium data sources available today, and there many new machine learning algorithms have been proposed, most of these researches do not focus on utilizing these data sources and algorithms. This paper proposes a stock market prediction service frame work that allows users to choose different data sources and machine learning techniques. In particular, while most existing prediction approaches are based on neural networks, support vector machines or Naive Bayes, we Proposed the flexibility of our framework by including metric learning based methods to predict stock movement.

Key Words: SAR – Stop & Reverse, SMA- Simple Moving Average.

INTRODUCTION

In proposed system we present the reliable and stable online Stock Market Prediction based application The subject input and questions , difficulty level will given as input from user Respected data will be stored on backend which is cloud . On backend admin will be manage tests related database work While user uses desktop application and by using various strategies accurate prediction is provided

BASIC IDEA

The goal is to improve the decision making power and

wakefulness about the investment in the stock market

from the naïve user’s perspective. The naïve investors are having the problem of choosing the valuable stock.

The reason for this concern is the lack of knowledge

about the market and lack of education. It is very

essentials to identify the stocks which are suitable for the investors’ Expectations and identify the suitable

stocks for short term investors and long term investors

based on the stock market analysis. Here We Proposed

an easiest way to identify the stocks which is suitable for the investors’ expectations. It means that this research

will give suitable stocks for short term investors and

long term investors based on the data analysis and its

wave pattern movement. And also it will give the details

regarding risk associated with these stocks.

IMPLEMENTATION

These problem have many methodologies to solve like predicting stock with strategies like Simple moving

average ,Parabolic SAR, Bolinger Bandes, Don chain Channels which gives prediction up to 70-80% while our application predicts up to 80-90% with somehow Accuracy.

Examples: A Simple Moving Average is formed by computing the average price of security over a specific number of periods. Most moving averages are based on closing price .A 5-day simple moving average is the five sum of closing prices divided by five. As its name implies, a moving average is an average that moves

Mathematical Model:

SMA = (P[1] + P[2] + P[3] + . . . P[N]) ÷N

Parabolic SAR - Parabolic Stop-and-Reverse, this indicator provides both entries and exits. The indicator is composed of a series of dots, either above or below the price. When dots move from below to above the price bars, it is time to get out of longs or get short.

Extreme Point (EP) = The highest high of the current uptrend or the lowest low of the current downtrend. Acceleration Factor (AF) = Determines the sensitivity of the SAR. AF starts at .02 and increases by .02 every time the EP rises in a Rising SAR or EP falls in a Falling SAR. The calculations for Rising Parabolic SAR and Falling Parabolic SAR are different so they will be separated.

Page 2: Stock market Prediction & Analysis - irjet.net · PDF fileInternational Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 01 | Jan-2018

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 05 Issue: 01 | Jan-2018 www.irjet.net p-ISSN: 2395-0072

© 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1437

Rising Parabolic SAR Previous SAR + Previous AF (Previous EP - Previous SAR) = Current SAR Falling Parabolic SAR Previous SAR - Previous AF (Previous SAR - Previous EP) = Current SAR

CONCLUSIONS Signal predicates the overall the value of the stocks according to the Buy-sell signal given by the software. Increasing the overall profitability of the user & Minimizing the Stop loss as per the particular stock.

REFERENCES [1] Mojgan Ghanavati, Raymond K. Wong, Fang Chen, Yang Wang, Simon Fong“A Generic Service Framework for Stock Market Prediction”-2016 [2] C.-J. Huang, D.-X. Yang, and Y.-T. Chuang, “Application of wrapper approach and composite classifier to the stock trend prediction,” Expert Systems with Applications, vol. 34, no. 4, pp. 2870–2878, 2008. [3] Y. Lin, H. Guo, and J. Hu, “Ansvm-based approach for stock market trend prediction,” in Neural Networks (IJCNN), The 2013 International Joint Conference on. IEEE, 2013, pp. 1–7.

BIOGRAPHIES

Ajinkya Hinge is currently a student of GESRHSCOE, Nashik of the Savitribai Phule Pune University.

Himanshu Chavan is currently a student of GESRHSCOE, Nashik of the Savitribai Phule Pune University.

Jantin Jantre is currently a student of GESRHSCOE, Nashik of the Savitribai Phule Pune University.

Parikshit Jagtap is currently a student of GESRHSCOE, Nashik of the Savitribai Phule Pune University.